The Four Anxieties of Engineering Leaders
And Why Software Engineering Needs a New Measurement Paradigm
Engineering leadership transcends technical expertise. It's about navigating the complex landscape of people, projects, and performance while consistently communicating value upward. Yet most engineering leaders lack the objective data needed to make informed decisions. Instead, they rely on gut feeling, fragmented tools, vanity metrics, and second-hand information.
After working with hundreds of engineering leaders across organizations of all sizes, we've identified a pattern: four fundamental anxieties that keep even the most talented engineering leaders awake at night. These anxieties stem from a single root cause—insufficient visibility into what's actually happening in their organizations.
Leading an engineering organization without true visibility is like steering a ship through fog. Let's explore the four primary anxieties and how they demand a new approach to measuring engineering progress.
The Four Anxieties
1. Who are my top performers, and are they at risk of burnout?
Your star engineers are the backbone of your organization, contributing 3-5× more value than the average team member. They consistently deliver high-quality work, mentor others, and drive projects forward.
The anxiety: Losing a top performer is catastrophic. When your best engineer burns out and leaves, the impact is devastating:
Critical knowledge vanishes, stalling key projects
Remaining teammates become overloaded, risking a burnout cascade
Replacements take months to ramp up, draining momentum
Most engineering leaders discover burnout too late—after the damage is done. The warning signs were there, but buried in data across multiple systems.
2. How do I push for excellence without sacrificing team health?
Engineering leaders face a fundamental tension: they must drive exceptional results while simultaneously nurturing a positive culture where everyone thrives.
The anxiety: You walk a tightrope between pushing for results and preserving team health. Finding this balance feels impossible:
Pushing too hard can lead to burnout, resentment, and eventual talent loss
Not pushing enough breeds missed opportunities and competitive disadvantage
Different team members require different types of support and motivation
Without objective data and insights, engineering leaders resort to one-size-fits-all approaches that either push too hard or not hard enough.
3. Are we going to ship our projects on time?
Engineering leaders are judged on their ability to deliver results predictably and reliably. Your reputation depends on predictable delivery.
The anxiety: Every engineering leader worries: will we ship on time? Missing deadlines cascades throughout the organization:
Sales, marketing, and leadership plans collapse when releases slip
Fundraising narratives falter if promised capabilities aren’t ready
Customer trust erodes with each broken commitment
Industry research reveals that 45% of tech product launches get delayed by at least a month. When this happens, your credibility is damaged, sometimes irreparably.
4. How do I report engineering progress upwards?
Engineering leaders must translate complex technical work into simple progress updates for executives and boards.
The anxiety: Translating engineering's complex work into simple progress updates is a constant challenge:
Executives expect the story in 30 seconds (or less)
Data lives in half a dozen systems and must be stitched together by hand
Hours spent on slide decks steal time from leading the team
This communication gap puts engineering in a difficult position: either spend hours creating reports or risk having your value questioned.
The Root Cause: Engineering Metrics are Broken
Most engineering teams rely on a standard toolkit of metrics to gauge productivity and success: story points and velocity from agile methodologies, lines of code or PR counts from version control systems, and deployment metrics from CI/CD pipelines. At first glance, these provide concrete numbers to track progress, but they create a fundamentally flawed picture of engineering performance.
The dominant metrics and processes that teams use are inefficient or simply broken in several ways:
Current metrics focus on activity, not impact:
Lines of code and PR counts reward volume, not value
Story points are subjective, and not correlated to outcomes
Cycle time measures speed, not effectiveness
Most engineering metrics are easily gameable:
Engineers can inflate story points to improve velocity
Small PRs boost metrics without increasing impact
"Easy" features first creates illusion of progress
Existing tools provide fragmented visibility:
Data scattered across JIRA, GitHub, CI/CD systems
Each tool offers only a narrow lens
Critical signals about team health get lost
Current metrics are partial and subjective:
Common metrics rarely correlate with business impact
Story points vary between teams, making comparison impossible
Numbers miss qualitative insights about actual accomplishments
Raw data fails to capture context, challenges, and innovations
Current metrics are blind to AI agents:
Can't distinguish AI-generated code from human effort
Initial “boosts” in productivity may hide time spent refining AI output
Code looks productive on paper but creates technical debt
Without truly objective data that measures what matters, engineering leaders are essentially flying blind. They struggle to confidently allocate resources, distinguish real outcomes from busywork, establish fair benchmarks, or identify burnout risks before losing their top performers.
The Path Forward: A New Measurement Paradigm
Engineering leaders deserve better than vanity metrics and fragmented insights. What's needed isn't just better tools but an entirely new measurement paradigm that addresses the root causes of these anxieties.
This new paradigm requires:
Value-oriented metrics that measure outcomes, not just activities—focusing on code impact, customer impact, business results, and engineering quality
Holistic team intelligence that combines quantitative data with qualitative insights to detect burnout signals, engagement patterns, and collaboration dynamics
Predictive delivery insights that analyze historical patterns and current work to provide realistic forecasts, not just roll-ups of individual estimates
Context-rich communication frameworks that automatically translate engineering complexity into business value stories for executive audiences
By addressing these core needs, engineering leaders can make better decisions, protect their teams, deliver more predictably, and effectively communicate their value to the rest of the organization.
At Maestro AI, we’ve built an AI-first approach to measure and track progress in software teams, motivated by the four anxieties we hear from engineering leaders. If any of this resonates, we want to hear your story! Schedule a chat with us.
Great post!